We conduct experiments on COCO dataset [1]}, where we train on 2017train (115k images) and evaluate results on both 2017val and 2017test-dev using the standard metrics. For further investigating segmentation performance with occlusion handling, we propose a subset split, called COCO-OCC, which contains 1,005 images extracted from the validation set (5k images) where the overlapping ratio between the bounding boxes of objects is at least 0.2. Segmenting COCO-OCC with highly overlapping objects is much more difficult than 2017val, where we observe a performance gap around 3.0\(AP\)  for the same model in the experiment section.
